For supply chain organizations, the rules of the road are changing. A surge of data and digitization is transforming supply chain management. At the same time, new expectations for sustainability and social responsibility are soaring among consumers and business leaders alike. Supply chain stakeholders must increasingly account for these emerging value systems and associated metrics. AI will play a significant role in harmonizing data and providing strategic insights. However, AI will only partially automate supply chains, and supply chain leaders will increasingly be challenged to define their teams' roles in working with AI to make complex decisions and trade-offs.

This AI revolution must be collaborative. AI’s capabilities may seem boundless, but AI models and tools also require intelligent, strategy-minded human operators to observe their outputs and define the direction of travel. Functions like AI governance and decision engineering will emerge as critical operational roles in the supply chain organizations of the future. The question isn’t just “How will AI capabilities transform supply chain management?” but also “What roles will humans play in AI-defined supply chains?”

How Will Humans Work with AI?

Beyond the current buzz around Generative AI's image and text generation capabilities, there’s a paradigm shift in information management behind the scenes. While AI relies on computing, in many ways, it’s a radical departure from traditional computing. When modern robots, for instance, rely on AI models, they don’t balance themselves using pre-programmed motion capabilities. Instead, a learning algorithm processes sensory inputs and iteratively improves. These new robots, much like humans, learn to climb stairs after several attempts and then retain this knowledge – a testament to the power of experiential learning. 

With AI, we are witnessing a shift from human-made static rules to observation-based learned behaviors. 

Similarly, the large language models (LLMs) that underly GenAI don’t operate on set rules or instructions. Instead, they leverage vast training data to learn and apply semantic logic probabilities.

With AI, we are witnessing a shift from human-made static rules (and the associated mathematics of rigid systems) to observation-based learned behaviors.

The implications for human-AI collaboration are two-fold:

  1. Humans no longer have to meticulously codify and build system rules, exceptions, or defined algorithms. Instead, the role of humans is shifting towards primarily directing AI towards the correct information to learn. Because AI programs are not mechanical or static, significant human effort will go toward running iterative AI experiments and connecting AI with new datasets.   
  2. The absence of codified behaviors can result in significant uncertainty. As impressive as the results of AI models are, they can be difficult to predict and explain. This means that parameters for human-directed AI governance and control are paramount.

These dynamics will impact all industries and functional roles, but they have unique implications for supply chain operations.

The Future Supply Chain: AI Meets Human Intelligence

Today’s supply chain operations often rely on management science dating back over a hundred years. Frederick Winslow Taylor’s “scientific management” was picked up by Henry Ford’s assembly line revolution, and many of these founding principles are still used today to calculate inventory levels, transportation lead times, and manufacturing throughput. The focus has always been on finding the best way to approximate real-world events and building models compatible with the most advanced computing methods.

These static supply chain models were highly explainable and contributed significantly to industrial efficiency throughout the 20th century. However, these static mathematical rules fail to accurately capture and manage the complexities of today’s market dynamics and related supply chain behaviors. Tomorrow’s supply chains will need AI because only AI can operate in this dynamic complexity and learn from large swathes of information to drive reliability and predictive and proscriptive insights in near real-time. 

In the agile, responsive, dynamic, data-rich, AI-driven supply chain of the future, the role of human operators is bound to change.

The latest strides in visualization and virtualization are opening up new possibilities, allowing us to create digital models of real-world entities such as products, transportation vessels, entire factories, and even complex systems like social and demographic entities. Intuitive, observation-based optimization and virtualization are ushering in a new era of “augmented humans” – individuals who can instantly connect to virtual worlds, explore them through simulations and scenarios, and manipulate and optimize real-world events at a scale and impact never seen before.

To illustrate some of the potential impact of these capabilities:

  • In distribution planning, there will be no need to manage individual deliveries against particular modes of transportation (air, ocean, road, etc.). Instead, the decision-making will be elevated to a portfolio manager (akin to a stock trader), who manages material flows for entire customer segments across deliveries and expected service levels.
  • Pricing and profitability management are shifting from static product-costing and moving average pricing to real-time dynamic profitability visibility, enabling instantaneous and automated trade-off decisions for promising orders and revenue predictions. Here, the human role will likely evolve toward monitoring how AI interprets and responds to supply chain-related events in real time and seeking creative new approaches to improve AI-driven systems iteratively.

 In the agile, responsive, dynamic, data-rich, AI-driven supply chain of the future, the role of human operators is bound to change. Time-consuming and inefficient information management and consensus-building will be replaced by a new role, “boundary condition management,” which is not dissimilar to driving an autonomous vehicle. Humans will guide the process towards the destination rather than actively driving and making every micro-decision. 

The transition from a traditional execution focus to a world where advanced technologies can learn and outperform almost any human is significant. Increasingly, thoughtful responses to the AI revolution are focusing on how AI provides an opportunity to recognize capabilities that are truly unique to humans. One of those fundamental human traits is our ability to question assumptions deeply. This suggests that, in an AI-defined supply chain, humans will always need to ask and refine the assumptions underlying AI models, continuously ensuring that enterprises are deploying this powerful tool responsibly.

New Responsibilities: Striving for Value, not just Speed and Revenue

Speed and cost efficiency will always be fundamental supply chain objectives. The synthesis of deep supply chain insights, virtualization simulation, and AI-driven optimization will certainly advance those objectives. But increasingly, other supply chain imperatives will also be in the mix. Supply chain organizations' values and strategic goals will need to be realigned to meet the evolving expectations of customers, investors, and regulators regarding sustainability and responsible use of energy resources.

AI may become particularly advantageous for managing this multivariable supply chain in the future. On-time delivery might be necessary, but what if it comes at a cost to other customers or the environment? What kind of arbitration or governance would we need to create to manage these situations? Supply chain organizations and regulators must also explore thorny issues around AI-driven competitive dynamics. Suppose numerous AI-driven supply chains compete for the same resources, markets, and customers. How do we ensure that AI-driven decision-making makes the right tradeoffs rather than devolving into a potentially “ruthless” optimization engine?

Defining new value concepts and operating boundaries for these advanced capabilities is a task uniquely suited to humans, and it will be a regular strategic focus in the future.  

Navigating a Radical New Supply Chain Landscape

Humans have always been explorers, and AI is simply a radically new landscape. Attempting to put AI back into the genie bottle would be futile. Recognizing this, Wipro has created a novel and innovative capability — Supply Chain 360 — that enables transformation pathways toward the AI-driven, human-centric supply chains of the future. Rather than scaling AI in ways that emphasize unfettered automation, we are seeking to balance AI's astounding supply chain capabilities with a vision for how human problem-solving and creativity will continue to add value in the context of supply chains.

The future supply chains should be efficient and economically viable while also aligning with core principles of human-centricity, resilience, and sustainability. To get there, we will need AI's efficiency and data processing capabilities, but we also need human guides who can direct AI toward the right objectives. What training, systems, and roles best combine AI tools and human supply chain talent? Supply chain leaders should ask that question as they envision future supply chain operations. 

About the Author

Sebastian Ennulat
Senior Partner & European Head of Wipro Supply Chain Consulting

Sebastian joined Wipro in 2010 and has over 25 years of experience leading digital supply chain transformations across manufacturing, hi-tech, CPG, retail, and utilities. He brings value to clients by leveraging data-driven innovations and generating rapid ROI.